agentic models research engineer

We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting. Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar). Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs. The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases. A product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work. At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.